U.S. patent number 10,198,475 [Application Number 14/607,681] was granted by the patent office on 2019-02-05 for database calculation engine having forced filter pushdowns with dynamic joins.
This patent grant is currently assigned to SAP SE. The grantee listed for this patent is Johannes Merx, Tobias Mindnich, Christoph Weyerhaeuser. Invention is credited to Johannes Merx, Tobias Mindnich, Christoph Weyerhaeuser.
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United States Patent |
10,198,475 |
Mindnich , et al. |
February 5, 2019 |
Database calculation engine having forced filter pushdowns with
dynamic joins
Abstract
A query that requests a filter attribute is received by a
database server from a remote application server that is associated
with a calculation scenario that defines a data flow model
including one or more calculation nodes. Subsequently, the database
server instantiates the calculation scenario. As part of the
instantiation, the calculation scenario is optimized by (i) pushing
down a filter attribute from a first node to a lowest available
child node, (ii) removing the filter attribute from the first node,
and (iii) removing non-required join-attributes from the
instantiated calculation scenarios. Thereafter, the operations
defined by the calculation nodes of the instantiated calculation
scenario can be executed to result in a responsive data set. Next,
the data set is provided to the application server by the database
server.
Inventors: |
Mindnich; Tobias (Sulzbach,
DE), Weyerhaeuser; Christoph (Heidelberg,
DE), Merx; Johannes (Heidelberg, DE) |
Applicant: |
Name |
City |
State |
Country |
Type |
Mindnich; Tobias
Weyerhaeuser; Christoph
Merx; Johannes |
Sulzbach
Heidelberg
Heidelberg |
N/A
N/A
N/A |
DE
DE
DE |
|
|
Assignee: |
SAP SE (Walldorf,
DE)
|
Family
ID: |
56433354 |
Appl.
No.: |
14/607,681 |
Filed: |
January 28, 2015 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20160217182 A1 |
Jul 28, 2016 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
16/24544 (20190101); G06F 16/2456 (20190101) |
Current International
Class: |
G06F
16/2455 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Mofiz; Apu M
Assistant Examiner: Samara; Husam Turki
Attorney, Agent or Firm: Mintz Levin Cohn Ferris Glovsky and
Popeo, P.C.
Claims
What is claimed is:
1. A method comprising: receiving, by a database server from a
remote application server, a query requesting a filter attribute
that is associated with a calculation scenario that defines a data
flow model that includes one or more calculation nodes arranged in
a hierarchy; instantiating, by the database server, the calculation
scenario, the instantiating of the calculation scenario optimizes
the calculation scenario by at least: in response to determining
that the filter attribute is not requested by one or more ancestor
calculation nodes of the calculation scenario, pushing down the
filter attribute to a lowest available descendent calculation node
of the calculation scenario; and in response to the filter
attribute being pushed down, removing the filter attribute from at
least one ancestor node of the lowest available descendent
calculation nodes that define a dynamic join operation, the dynamic
join operation joining a first table and a second table based on a
plurality of join attributes, the removal of the filter attribute
comprising removing the filter attribute as one of the plurality of
join attributes for joining the first table and the second table,
and the removal of the filter attribute as the one of the plurality
of join attributes reducing an intermediate result of one or more
calculation nodes comprising the data flow model; executing, by the
database server, the query by at least performing the operations
defined by the calculation nodes of the optimized calculation
scenario, the executing of the query resulting in a responsive data
set; and providing, by the database server to the application
server, the data set.
2. A method as in claim 1, wherein at least a portion of paths
and/or attributes defined by the calculation scenario are not
required to respond to the query, and wherein the instantiated
calculation scenario omits the paths and attributes defined by the
calculation scenario that are not required to respond to the
query.
3. A method as in claim 1, wherein at least one of the calculation
nodes filters results obtained from the database server.
4. A method as in claim 1, wherein at least one of the calculation
nodes sorts results obtained from the database server.
5. A method as in claim 1, wherein the calculation scenario is
instantiated in a calculation engine layer by a calculation
engine.
6. A method as in claim 5, wherein the calculation engine layer
interacts with a physical table pool and a logical layer, the
physical table pool comprising physical tables containing data to
be queried, and the logical layer defining a logical metamodel
joining at least a portion of the physical tables in the physical
table pool.
7. A method as in claim 6, wherein the calculation engine invokes
an SQL processor for executing set operations.
8. A method as in claim 1, wherein an input for each calculation
node comprises one or more of: a physical index, a join index, an
OLAP index, and another calculation node.
9. A method as in claim 8, wherein each calculation node has at
least one output table that is used to generate the data set.
10. A method as in claim 9, wherein at least one calculation node
consumes an output table of another calculation node.
11. A method as in claim 1, wherein the executing comprises:
forwarding the query to a calculation node in the calculation
scenario that is identified as a default node if the query does not
specify a calculation node at which the query should be
executed.
12. A method as in claim 1, wherein the calculation scenario
comprises database metadata.
13. A non-transitory computer program product storing instructions
which, when executed by at least one data processor forming part of
at least one computing device, result in operations comprising:
receiving, by a database server from a remote application server, a
query requesting a filter attribute that is associated with a
calculation scenario that defines a data flow model that includes
one or more calculation nodes arranged in a hierarchy;
instantiating, by the database server, the calculation scenario,
the instantiating of the calculation scenario optimizes the
calculation scenario by at least: in response to determining that
the filter attribute is not requested by one or more ancestor
calculation nodes of the calculation scenario, pushing down the
filter attribute to a lowest available descendent calculation node
of the calculation scenario; and in response to the filter
attribute being pushed down, removing the filter attribute from at
least one ancestor node of the lowest available descendent
calculation nodes that define a dynamic join operation, the dynamic
join operation joining a first table and a second table based on a
plurality of join attributes, the removal of the filter attribute
comprising removing the filter attribute as one of the plurality of
join attributes for joining the first table and the second table,
and the removal of the filter attribute as the one of the plurality
of join attributes reducing an intermediate result of one or more
calculation nodes comprising the data flow model; executing, by the
database server, the query by at least performing the operations
defined by the calculation nodes of the optimized calculation
scenario, the executing of the query resulting in a responsive data
set; and providing, by the database server to the application
server, the data set.
14. A computer program product as in claim 13, wherein at least a
portion of paths and/or attributes defined by the calculation
scenario are not required to respond to the query, and wherein the
instantiated calculation scenario omits the paths and attributes
defined by the calculation scenario that are not required to
respond to the query.
15. A computer program product as in claim 13, wherein at least one
of the calculation nodes filters results obtained from the database
server.
16. A computer program product as in claim 13, wherein at least one
of the calculation nodes sorts results obtained from the database
server.
17. A computer program product as in claim 13, wherein the
calculation scenario is instantiated in a calculation engine layer
by a calculation engine.
18. A computer program product as in claim 17, wherein the
calculation engine layer interacts with a physical table pool and a
logical layer, the physical table pool comprising physical tables
containing data to be queried, and the logical layer defining a
logical metamodel joining at least a portion of the physical tables
in the physical table pool.
19. A computer program product as in claim 18, wherein the
calculation engine invokes an SQL processor for executing set
operations.
20. A system comprising: at least one data processor; and memory
storing instructions which, when executed by the at least one data
processor, result in operations comprising: receiving, by a
database server from a remote application server, a query
requesting a filter attribute that is associated with a calculation
scenario that defines a data flow model that includes one or more
calculation nodes arranged in a hierarchy; instantiating, by the
database server, the calculation scenario, the instantiating of the
calculation scenario optimizes the calculation scenario by at
least: in response to determining that the filter attribute is not
requested by one or more ancestor calculation nodes of the
calculation scenario, pushing down the filter attribute to a lowest
available descendent calculation node of the calculation scenario;
and in response to the filter attribute being pushed down, removing
the filter attribute from at least one ancestor node of the lowest
available descendent calculation nodes that define a dynamic join
operation, the dynamic join operation joining a first table and a
second table based on a plurality of j oin attributes, the removal
of the filter attribute comprising removing the filter attribute as
one of the plurality of j oin attributes for joining the first
table and the second table, and the removal of the filter attribute
as the one of the plurality of j oin attributes reducing an
intermediate result of one or more calculation nodes comprising the
data flow model; executing, by the database server, the query by at
least performing the operations defined by the calculation nodes of
the optimized calculation scenario, the executing of the query
resulting in a responsive data set; and providing, by the database
server to the application server, the data set.
Description
TECHNICAL FIELD
The subject matter described herein relates to a database system
that incorporates a calculation engine that instantiates
calculation scenarios in which, as part of the instantiation
process, filters are pushed down to a lowest possible level as part
of dynamic joins.
BACKGROUND
Data flow between an application server and a database server is
largely dependent on the scope and number of queries generated by
the application server. Complex calculations can involve numerous
queries of the database server which in turn can consume
significant resources in connection with data transport as well as
application server-side processing of transported data. Calculation
engines can sometimes be employed by applications and/or domain
specific languages in order to effect such calculations. Such
calculation engines can execute calculation models/scenarios that
comprise a plurality of hierarchical calculation nodes.
SUMMARY
In one aspect, a query that requests a filter attribute is received
by a database server from a remote application server that is
associated with a calculation scenario that defines a data flow
model including one or more calculation nodes. Subsequently, the
database server instantiates the calculation scenario. As part of
the instantiation, the calculation scenario is optimized by (i)
pushing down a filter attribute from a first node to a lowest
available child node, (ii) removing the filter attribute from the
first node, and (iii) removing non-required join-attributes from
the instantiated calculation scenarios. Thereafter, the operations
defined by the calculation nodes of the instantiated calculation
scenario can be executed to result in a responsive data set. Next,
the data set is provided to the application server by the database
server.
At least a portion of paths and/or attributes defined by the
calculation scenario can, in some implementations, not be required
to respond to the query. In such cases, the instantiated
calculation scenario can omit the paths and attributes defined by
the calculation scenario that are not required to respond to the
query.
At least one of the calculation nodes can filter results obtained
from the database server. At least one of the calculation nodes can
sort results obtained from the database server.
The calculation scenario can be instantiated in a calculation
engine layer by a calculation engine. The calculation engine layer
can interact with a physical table pool and a logical layer. The
physical table pool can include physical tables containing data to
be queried, and the logical layer can define a logical metamodel
joining at least a portion of the physical tables in the physical
table pool. The calculation engine can invoke an SQL processor for
executing set operations.
An input for each calculation node can include one or more of: a
physical index, a join index, an OLAP index, and another
calculation node. Some or all calculation nodes can have at least
one output table that is used to generate the data set. At least
one calculation node can consume an output table of another
calculation node.
The query can be forwarded to a calculation node in the calculation
scenario that is identified as a default node if the query does not
specify a calculation node at which the query should be executed.
The calculation scenario can include database metadata.
Computer program products are also described that comprise
non-transitory computer readable media storing instructions, which
when executed one or more data processors of one or more computing
systems, causes at least one data processor to perform operations
herein. Similarly, computer systems are also described that may
include one or more data processors and a memory coupled to the one
or more data processors. The memory may temporarily or permanently
store instructions that cause at least one processor to perform one
or more of the operations described herein. In addition, methods
can be implemented by one or more data processors either within a
single computing system or distributed among two or more computing
systems. Such computing systems can be connected and can exchange
data and/or commands or other instructions or the like via one or
more connections, including but not limited to a connection over a
network (e.g. the Internet, a wireless wide area network, a local
area network, a wide area network, a wired network, or the like),
via a direct connection between one or more of the multiple
computing systems, etc.
The subject matter described herein provides many advantages. For
example, the current subject matter allows for filters specified by
a query to be pushed down to a lowest level possible as part of a
calculation scenario instantiation process even when dynamic joins
are employed. As a result, unnecessary intermediate results can be
avoided which, in turn, results in more efficient query
processing.
The details of one or more variations of the subject matter
described herein are set forth in the accompanying drawings and the
description below. Other features and advantages of the subject
matter described herein will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF DRAWINGS
FIG. 1 is a process flow diagram illustrating execution of a
calculation scenario having a dynamic top operator;
FIG. 2 is a diagram illustrating a calculation engine layer, a
logical layer, a physical table pool and their
interrelationship;
FIG. 3 is a diagram illustrating an architecture for processing and
execution control;
FIG. 4 is a diagram illustrating a first calculation scenario and a
corresponding instantiation of the first calculation scenario;
FIG. 5 is a diagram illustrating a second calculation scenario and
a corresponding instantiation of the second calculation scenario
having a forced filter pushdown and that removes unnecessary join
attributes;
FIG. 6 is a diagram illustrating a third calculation scenario;
FIG. 7 is a diagram illustrating a first instantiation of the third
calculation scenario of FIG. 6; and
FIG. 8 is a diagram illustrating a second instantiation of the
third calculation scenario of FIG. 6 having a forced filter
pushdown and that removes unnecessary join attributes.
DETAILED DESCRIPTION
FIG. 1 is a process flow diagram 100 illustrating a method in
which, at 110, a query requesting a filter attribute is received by
a database server from a remote application server that is
associated with a calculation scenario that defines a data flow
model including one or more calculation nodes. Subsequently, at
120, the database server instantiates the calculation scenario. As
part of the instantiation, the calculation scenario is optimized by
(i) pushing down a filter attribute from a first node to a lowest
available child node, (ii) removing the filter attribute from the
first node, and (iii) removing non-required join-attributes from
the instantiated calculation scenarios. Thereafter, at 130, the
operations defined by the calculation nodes of the instantiated
calculation scenario can be executed to result in a responsive data
set. Next, at 140, the data set is provided to the application
server by the database server.
FIG. 2 is a diagram 200 that illustrates a database system in which
there are three layers, a calculation engine layer 210, a logical
layer 220, and a physical table-pool 230. Calculation scenarios can
be executed by a calculation engine which can form part of a
database or which can be part of the calculation engine layer 210
(which is associated with the database). The calculation engine
layer 210 can be based on and/or interact with the other two
layers, the logical layer 220 and the physical table pool 230. The
basis of the physical table pool 230 consists of physical tables
(called indexes) containing the data. Various tables can then be
joined using logical metamodels defined by the logical layer 220 to
form a new index. For example, the tables in a cube (OLAP view) can
be assigned roles (e.g., fact or dimension tables) and joined to
form a star schema. It is also possible to form join indexes, which
can act like database view in environments such as the Fast Search
Infrastructure (FSI) by SAP AG.
As stated above, calculation scenarios can include individual
calculation nodes 211-214, which in turn each define operations
such as joining various physical or logical indexes and other
calculation nodes (e.g., CView 4 is a join of CView 2 and CView 3).
That is, the input for a calculation node 211-214 can be one or
more physical, join, or OLAP views or calculation nodes. A
calculation node as used herein represents a operation such as a
projection, aggregation, join, union, minus, intersection, and the
like. Additionally, as described below, in addition to a specified
operation, calculation nodes can sometimes be enhanced by filtering
and/or sorting criteria. In some implementations, calculated
attributes can also be added to calculation nodes.
In calculation scenarios, two different representations can be
provided. First, a stored ("pure") calculation scenario in which
all possible attributes are given. Second, an instantiated/executed
model that contains only the attributes requested in the query (and
required for further calculations). Thus, calculation scenarios can
be created that can be used for various queries. With such an
arrangement, calculation scenarios can be created which can be
reused by multiple queries even if such queries do not require
every attribute specified by the calculation scenario. For
on-the-fly scenarios this means that the same calculation scenario
(e.g., in XML format, etc.) can be used for different queries and
sent with the actual query. The benefit is that on application
server side the XML description of a calculation scenario can be
used for several queries and thus not for each possible query one
XML has to be stored.
Further details regarding calculation engine architecture and
calculation scenarios can be found in U.S. Pat. No. 8,195,643, the
contents of which are hereby fully incorporated by reference.
FIG. 3 is a diagram 300 illustrating a sample architecture for
request processing and execution control. As shown in FIG. 3,
artifacts 305 in different domain specific languages can be
translated by their specific compilers 310 into a common
representation called a "calculation scenario" 315 (illustrated as
a calculation model). To achieve enhanced performance, the models
and programs written in these languages are executed inside the
database server. This arrangement eliminates the need to transfer
large amounts of data between the database server and the client
application. Once the different artifacts 305 are compiled into
this calculation scenario 315, they can be processed and executed
in the same manner. The execution of the calculation scenarios 315
is the task of a calculation engine 320.
The calculation scenario 315 can be a directed acyclic graph with
arrows representing data flows and nodes that represent operations.
Each calculation node has a set of inputs and outputs and an
operation that transforms the inputs into the outputs. In addition
to their primary operation, each calculation node can also have a
filter condition for filtering the result set. The inputs and the
outputs of the operations can be table valued parameters (i.e.,
user-defined table types that are passed into a procedure or
function and provide an efficient way to pass multiple rows of data
to the application server). Inputs can be connected to tables or to
the outputs of other calculation nodes. Calculation scenarios 315
can support a variety of node types such as (i) nodes for set
operations such as projection, aggregation, join, union, minus,
intersection, and (ii) SQL nodes that execute a SQL statement which
is an attribute of the node. In addition, to enable parallel
execution, a calculation scenario 315 can contain split and merge
operations. A split operation can be used to partition input tables
for subsequent processing steps based on partitioning criteria.
Operations between the split and merge operation can then be
executed in parallel for the different partitions. Parallel
execution can also be performed without split and merge operation
such that all nodes on one level can be executed in parallel until
the next synchronization point. Split and merge allows for
enhanced/automatically generated parallelization. If a user knows
that the operations between the split and merge can work on
portioned data without changing the result he or she can use a
split. Then, the nodes can be automatically multiplied between
split and merge and partition the data.
Calculation scenarios 315 are more powerful than traditional SQL
queries or SQL views for many reasons. One reason is the
possibility to define parameterized calculation schemas that are
specialized when the actual query is issued. Unlike a SQL view, a
calculation scenario 315 does not describe the actual query to be
executed. Rather, it describes the structure of the calculation.
Further information is supplied when the calculation scenario is
executed. This further information can include parameters that
represent values (for example in filter conditions). To obtain more
flexibility, it is also possible to refine the operations when the
model is invoked. For example, at definition time, the calculation
scenario 315 may contain an aggregation node containing all
attributes. Later, the attributes for grouping can be supplied with
the query. This allows having a predefined generic aggregation,
with the actual aggregation dimensions supplied at invocation time.
The calculation engine 320 can use the actual parameters, attribute
list, grouping attributes, and the like supplied with the
invocation to instantiate a query specific calculation scenario
315. This instantiated calculation scenario 315 is optimized for
the actual query and does not contain attributes, nodes or data
flows that are not needed for the specific invocation.
When the calculation engine 320 gets a request to execute a
calculation scenario 315, it can first optimize the calculation
scenario 315 using a rule based model optimizer 322. Examples for
optimizations performed by the model optimizer can include "pushing
down" filters and projections so that intermediate results 326 are
narrowed down earlier, or the combination of multiple aggregation
and join operations into one node. The optimized model can then be
executed by a calculation engine model executor 324 (a similar or
the same model executor can be used by the database directly in
some cases). This includes decisions about parallel execution of
operations in the calculation scenario 315. The model executor 324
can invoke the required operators (using, for example, a
calculation engine operators module 328) and manage intermediate
results. Most of the operators are executed directly in the
calculation engine 320 (e.g., creating the union of several
intermediate results). The remaining nodes of the calculation
scenario 315 (not implemented in the calculation engine 320) can be
transformed by the model executor 324 into a set of logical
database execution plans. Multiple set operation nodes can be
combined into one logical database execution plan if possible.
The model optimizer 322 can be configured to enable dynamic
partitioning based on one or more aspects of a query and/or
datasets used by queries. The model optimizer can implement a
series of rules that are triggered based on attributes of incoming
datasets exceeding specified thresholds. Such rules can, for
example, apply thresholds each with a corresponding a
parallelization factor. For example, if the incoming dataset has 1
million rows then two partitions (e.g., parallel jobs, etc.) can be
implemented, or if the incoming dataset has five million rows then
five partitions (e.g., parallel jobs, etc.) can be implemented, and
the like.
The attributes of the incoming datasets utilized by the rules of
model optimizer 322 can additionally or alternatively be based on
an estimated and/or actual amount of memory consumed by the
dataset, a number of rows and/or columns in the dataset, and the
number of cell values for the dataset, and the like.
The calculation engine 320 typically does not behave in a
relational manner. The main reason for this is the instantiation
process. The instantiation process can transform a stored
calculation model 315 to an executed calculation model 315 based on
a query on top of a calculation view which is a (catalog) column
view referencing one specific node of a stored calculation model
315. Therefore, the instantiation process can combine the query and
the stored calculation model and build the executed calculation
model.
The main difference between a relational view or SQL with
subselects and a calculation model is that the projection list in a
relational view is stable also if another SQL statement is stacked
on top whereas in a calculation model the projection list of each
calculation node in the calculation model is depending on the
projection list of the query or the parent calculation node(s).
With a calculation model 315, a user can provide a set of
attributes/columns on each calculation node that can be used by the
next calculation node or the query. If attributes/columns are
projected in a query or on the parent calculation node, then just a
subset of these requested attributes/columns can be considered in
the executed calculation model.
The calculation engine 320 can offer model designers the
possibility to enrich their data model with a forced filter
pushdown semantic which can be seen as a generic and flexible way
to express a filter injection. In contrast to simply allowing a
user to specify a filter condition on a specific node level, the
current subject matter is directed to a forced filter pushdown
feature. With a forced filter pushdown feature, if attributes are
flagged for forced pushdown in a query, the calculation engine 320
can ensure that all attributes are removed on the defined data flow
graph (as part of the instantiation process). In some variations, a
runtime error can be indicated if attributes are still required on
the defined data flow path. With such functionality, users can
easily access the logic in normal SQL WHERE conditions and, in some
cases, specifically request such attributes.
Forced filter pushdowns can only be successful applied if
attributes are not needed in any top level operator of the data
flow model which easily can be the case if marked attributes are
used as join attributes. FIG. 4 is a diagram 400 illustrating a
calculation model 410 and a corresponding instantiated calculation
model 420. The calculation model 410 includes a projection node 412
that enables a projection of A, B, C, X, Y which requires a dynamic
join via node 414 on Table 1 416 and Table 2 418 along attributes
A, B. In response to receiving a query: SELECT X, Y, B FROM VIEW
WHERE A=1, the calculation model 420 is instantiated so that the
requested data can be obtained from Table 1 416 and Table 2 418. As
illustrated in the instantiation 420, attribute A is only used for
filtering (at the request node 422) and thus is not requested on
the top level operator. As the filter evaluation in the
instantiated calculation model 420 requires A to be requested on
all child operators (dynamic join 422) and, furthermore column A is
defined as join-attribute on the join node it can be seen that
using attributes as forced filter pushdown and join attributes at
the same time is not possible in most use cases.
In some implementations, the calculation engine 320 can utilize
different join optimization techniques like join removal or so
called dynamic joins. More information about dynamic joins can be
found, for example, in U.S. patent application Ser. No. 14/083,267
entitled "Join Optimization in a Database" filed on Nov. 13, 2014,
the content of which is hereby incorporated by reference. In
specific calculation scenarios 315 and data models, dynamic joins
can be very useful for model designers to achieve significant
performance improvements--mainly caused by their ability to
eliminate additional join-attributes that are not requested by the
query. However, as the instantiation process of calculation engine
320 usually will also request filter attributes on lower levels (as
illustrated in FIG. 4) and the attribute usage in the initial user
query is hidden on lower levels of the execution model, filter
attributes will also be used as join-attribute on dynamic join
operations.
In order to enable the full functionality of both features and
providing users the possibility to combine dynamic joins and the
forced filter pushdown semantic in an easy and very flexible, the
functionality of dynamic joins can be extended with the forced
filter pushdown feature as provided herein.
FIG. 5 is a diagram 500 illustrating the calculation model 410 of
FIG. 4 and a corresponding instantiated calculation model 520 in
response to receiving a query: SELECT X, Y, B FROM VIEW WHERE A=1.
Attributes which are flagged for a forced filter push down and
which can be successful removed from the dataflow graph, are not
used as join-attributes on joins any more, as illustrated in the
instantiated calculation model 520. In order to provide such
functionality, the calculation engine 320 can consider all dynamic
join operators during the runtime evaluation of the enforced filter
push down. If a filter attribute can be pushed down to a child
operator and successfully removed on the same node, the evaluation
logic can also consider and rewrite dynamic join operations by
removing non required join-attributes from the instantiated
calculation model 520.
In this example, query requests a projection along attributes B, X,
Y including a filter on attribute A which is marked for a forced
filter push down (at node 522). The filter is pushed down to the
table level (both Table 1 416 and Table 2 418 have the filter A=1)
and, additionally, attribute A is removed from the dynamic join
operation at node 524, thereby reducing intermediate results and
changing the join operation semantic. By providing this extended
behavior, users can mix dynamic joins and forced filter push down
semantics at the same time and therefore are able to express very
flexible model representations in their calculation models 315.
FIG. 6 is a diagram 600 of an example stored calculation scenario.
With this calculation scenario, two tables 630, 640 are dynamically
joined 620 as part of an aggregation operation 610. FIG. 7 is a
diagram 700 illustrating an instantiated calculation scenario
(i.e., runtime model) that corresponds to the calculation scenario
of FIG. 6 that does not include a forced filter pushdown but
includes a dynamic join. With FIG. 7, for the query: SELECT SUM
(Sales), Product, Product_Desc FROM CALC_SCENARIO GROUP BY Product,
Product_Desc, WHERE Region=US, the filter attribute: Region=US is
part of the request node 710. In addition, the inner join (dynamic
join) 730 and the aggregation node 720 include the Region
attribute.
FIG. 8 is a diagram 800 which, in contrast to that of FIG. 7,
includes an instantiated calculation scenario that corresponds to
the calculation scenario of FIG. 6 that includes a forced filter
pushdown on attribute Region. In this case, the filter Region=US is
pushed down to the lowest available child node, namely the
underlying tables 840, 850. Further, the Region attribute is
removed from both the inner join node 830 and the aggregation node
820. Therefore, the Region attribute does not form part of either
of the join or aggregation operations which results in more
efficient and more rapid query processing.
One or more aspects or features of the subject matter described
herein may be realized in digital electronic circuitry, integrated
circuitry, specially designed ASICs (application specific
integrated circuits), computer hardware, firmware, software, and/or
combinations thereof. These various implementations may include
implementation in one or more computer programs that are executable
and/or interpretable on a programmable system including at least
one programmable processor, which may be special or general
purpose, coupled to receive data and instructions from, and to
transmit data and instructions to, a storage system, at least one
input device (e.g., mouse, touch screen, etc.), and at least one
output device.
These computer programs, which can also be referred to as programs,
software, software applications, applications, components, or code,
include machine instructions for a programmable processor, and can
be implemented in a high-level procedural language, an
object-oriented programming language, a functional programming
language, a logical programming language, and/or in
assembly/machine language. As used herein, the term
"machine-readable medium" refers to any computer program product,
apparatus and/or device, such as for example magnetic discs,
optical disks, memory, and Programmable Logic Devices (PLDs), used
to provide machine instructions and/or data to a programmable
processor, including a machine-readable medium that receives
machine instructions as a machine-readable signal. The term
"machine-readable signal" refers to any signal used to provide
machine instructions and/or data to a programmable processor. The
machine-readable medium can store such machine instructions
non-transitorily, such as for example as would a non-transient
solid state memory or a magnetic hard drive or any equivalent
storage medium. The machine-readable medium can alternatively or
additionally store such machine instructions in a transient manner,
such as for example as would a processor cache or other random
access memory associated with one or more physical processor
cores.
To provide for interaction with a user, the subject matter
described herein can be implemented on a computer having a display
device, such as for example a cathode ray tube (CRT) or a liquid
crystal display (LCD) monitor for displaying information to the
user and a keyboard and a pointing device, such as for example a
mouse or a trackball, by which the user may provide input to the
computer. Other kinds of devices can be used to provide for
interaction with a user as well. For example, feedback provided to
the user can be any form of sensory feedback, such as for example
visual feedback, auditory feedback, or tactile feedback; and input
from the user may be received in any form, including, but not
limited to, acoustic, speech, or tactile input. Other possible
input devices include, but are not limited to, touch screens or
other touch-sensitive devices such as single or multi-point
resistive or capacitive trackpads, voice recognition hardware and
software, optical scanners, optical pointers, digital image capture
devices and associated interpretation software, and the like.
The subject matter described herein may be implemented in a
computing system that includes a back-end component (e.g., as a
data server), or that includes a middleware component (e.g., an
application server), or that includes a front-end component (e.g.,
a client computer having a graphical user interface or a Web
browser through which a user may interact with an implementation of
the subject matter described herein), or any combination of such
back-end, middleware, or front-end components. The components of
the system may be interconnected by any form or medium of digital
data communication (e.g., a communication network). Examples of
communication networks include a local area network ("LAN"), a wide
area network ("WAN"), and the Internet.
The computing system may include clients and servers. A client and
server are generally remote from each other and typically interact
through a communication network. The relationship of client and
server arises by virtue of computer programs running on the
respective computers and having a client-server relationship to
each other.
The subject matter described herein can be embodied in systems,
apparatus, methods, and/or articles depending on the desired
configuration. The implementations set forth in the foregoing
description do not represent all implementations consistent with
the subject matter described herein. Instead, they are merely some
examples consistent with aspects related to the described subject
matter. Although a few variations have been described in detail
above, other modifications or additions are possible. In
particular, further features and/or variations can be provided in
addition to those set forth herein. For example, the
implementations described above can be directed to various
combinations and subcombinations of the disclosed features and/or
combinations and subcombinations of several further features
disclosed above. In addition, the logic flow(s) depicted in the
accompanying figures and/or described herein do not necessarily
require the particular order shown, or sequential order, to achieve
desirable results. Other implementations may be within the scope of
the following claims.
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